MLOps & Industrial AI Are Progressing Quickly and Are Unstoppable by@dev-technosys

MLOps & Industrial AI Are Progressing Quickly and Are Unstoppable

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I always wonder what will be next, but I got my answer when I learned that AI & MLOps are both unstoppable. Both will change the whole scenario of the industries, even the latest trends that we are observing. The continuous evolution in DevOps with the combination of Machine Learning for corporate application development is a solid reason.

The real-time example is the healthcare sector. Many articles on the internet reported that patient experience and medicine practice have changed using modern-age techniques such as AI ML or chatbot in the healthcare industry. Another best example is the manufacturing sector that is progressing in Industry 4.0. Now, industries do not hesitate to use smart devices, sensors, and tech-oriented processes for smart manufacturing.

I am more excited to share with the readers the advantages of MLOps, such as:

  • The machine learning lifecycle completely supports rapid innovation.
  • The ML allows streamlined changes effortlessly.
  • It eliminates all bottleneck issues that can resist the businesses for AI transformation.
  • Development, delivery, deployment, and management are four pillars of industries that can be optimized with the help of Machine Learning in great terms.
  • It allows human experts to monitor the production models.

For novice readers here, I will mention the basic definitions of AI & ML that will help make them more comfortable with the topic that we are reading here.

  • Machine Learning

Most of the time, you will find Machine Learning and Artificial Intelligence together. The reason is ML is one of the branches of AI, where other branches are Natural Language Processing, Artificial Neural Network (ANN), and Robotics.

In simplistic terms, its definition can be understood as following the concept of ML; machines learn from the data instead of obvious programming. However, the process is complex and requires the efforts of Data Scientists and AI developers.

As per statistics published on Statista.com, in the year 2021, among all the use cases, the major focus was on improving customer experience (57%). The other ML use cases were customer retaining, fraud detection, building brand awareness, and others.

  • Artificial Intelligence

The definition of AI is straightforward to understand that making a machine act with human intelligence. It is one of the technologies that is progressing continuously and even a most popular topic of debate. The credit goes to Alan Turing and Jhon McCarthy. The ultimate goal of AI is to make a Machine Smart.

According to the current scenario, AI is not fiction or imagination. It is a reality that we can observe in our surroundings. Every day, AI researchers are trying to make the machine more intelligent than its previous version.

One of the fantastic websites to read about scientific facts and research-oriented news –ScienceDaily, published on 11th Feb 2022 that wearable armbands can help have the grip for persons living with prosthetic hands. Previously on 8th Feb, they published that self-sensing robots are now equipped with electrochemically driven pumps. In combination with Big Data & AI, wildlife preservation can happen, including larger data sets and smart tracking devices.

It is only one example; other examples also exist. These examples are not for filling the content or making this article lengthier. It is for the new readers to understand the 360-degree scope of AI. Later it will help them to co-relate with MLOPs and Industrial AI.

The Curiosity about MLOps and Its Stages

Not only the readers but also businesses are curious about MLOps. They are trying to find how industrial AI & MLOps can help them for future transformation. Before we proceed with core information here, I would like to mention the fact based on the report published by Deloitte. It says that MLOps will be expanded up to $4 billion by 2025.

In the upper section, we have gone through the basic definition, and this section will take you through the MLOps and its stages.

The MLOps definition says that it is the process for streamlining model production with quality via following the automation process. Data scientists and engineer involves in it. It offers several benefits to businesses, from experiments to managing regulatory requirements.

Let’s take a break from the topic for a while; here, I am going to mention some relevant case studies on the topic. Some of the best case studies of Machine Learning with AI implemented by IBM are Wunderman Thompson and Humana.

i. The Wunderman Thompson works as a growth partner and consultancy providing branding-oriented solutions, including CRM, CX, and technology. They leverage ML for brand promotion, new customers on-boarding, and data management.

ii. We can’t detach the ML with AI as AI is the root of the ML, so you will be surprised to know what conversational AI can promise. Humana is a leading health insurance company with 13 million customers in the USA reduced the cost of the preservice call with the help of a conversational AI solution by IBM.

These case studies can be considered as fine examples and indicate the hidden possibilities for industries.

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Let’s come back to the topic and check out the following stages of MLOps.

  • Use Case Discovery: In the collaborative process, the data scientists and businesses first define the problem, convert it into a problem statement, and then define the objectives that ML will fulfill.
  • Data Engineering: Again, the key player is a collaboration between data scientists and engineers. Usually, they collect, organize and process the data for modeling.
  • Machine Learning Pipeline: the machine learning pipeline in integration with CI/CD ensures fine data flow. The data is being used to train the models. The data scientists used to do experiments with it. It works like whenever new data is added, the model retraining starts.
  • Production Deployment: It stands clearly with the method that lets the experts integrate the Machine Learning model into the existing production system and environment for practical use. The production deployment can be done in three ways: web services used for prediction, on-demand batch prediction, and edge device computing.
  • Production Monitoring: This stage is to find out whether a model is working as per expectations or not. The experts using new algorithms and data performs experiments. For production, monitoring memory and compute requirements are also important terms.

How MLOps help the businesses for future transformation?

MLOPs are progressing speedy, and very soon, there will be more changes in the market segment. Most of the large corporations are welcoming Machine Learning Operations, and the only reason is to gain a cutting edge in the competition. Among its several advantages, one of the key advantages I would love to share is that it smooths the strategy planning and assist the managers in implementing it in a fantastic way.

An answer to the question asked starting at this segment can be found from the subsequent advantages of MLOps below-

  • Improved Life Cycle Management

Since the launch of machine learning, it has been proved that it works great when it comes to automation. On the other hand, automation is a promising process that helps to improve the lifecycle. It reduces the time that an enterprise spends in doing manual efforts. The MLOps facilitates to automate most of the manual work flawless. In-directly, it also reduces the cost.

  • Improved Communications

Communication between person to person or department to department ties the whole organization strongly. The MLOps helps enterprises establish a robust communication channel and eliminates bottleneck issues.

It is best to create a set of operations for each employee to follow that removes the chances of any operational redundancy. After preparing the checklist, a manager can send it to all employees. After finishing it, they can modify and share it again.

  • Focused Collaboration Via MLOps

By extending the above point, communication and collaboration are crucial for any organization. The absence of collaboration always creates space for blunders that you and I would not like to have. So, again here comes MLOps to rescue us from the blunders, and the reason is it works fine regarding collaboration requirements.

One department can share the task with another where all stakeholders can observe what has been done and what is coming next. It works like employees and managers can access the earlier communication and checklist in the pipeline. It reduces response waiting time and handles inefficient e-mails manner.

  • Improved Regulatory Compliance

It is another fantastic advantage of MLOps that it helps regulate compliance in an improved way. On the other hand, it creates wonders in transforming Industrial Workflow with Artificial Intelligence. Due to the increasing demands regulating rules are more stringent. MLOps can help you to implement the models according to the required standards.

  • Streamlined Workflow

Automation is one of the qualities of AI & ML that provides 360-degree freedom to organizations to attain operational efficiency. It supports both types of operations, i.e., internal and external. On the other hand, taking real-time feedback is so much easy. MLOps also empowers businesses to take real-time feedback from their workforce. In conclusion, It can be said that machine learning operations help in streamlining the workflows.

  • Implementation and Deployment With Ease

This point you can consider to dedicate to all above points. Still, confused so let me clear it. In the above points, we have discussed the advantages of improved life cycle management, improved communications, focused collaboration via MLOps, improved regulatory compliance, and streamlined workflow.  All these advantages in turn provide the ease to implement and deploy the machine learning models with ease.

The Final Words from My Side-

Because MLOps and Industrial AI is such a vast topic to discuss, read, and write about, you can also find more information on the internet about it. The point is MLOps & Industrialized is much needed for operational discipline. It remains beneficial in several terms. Most of the bigger brands have adopted it, and some of the others are in the process of adoption. The future will be more bright with MLOPs.

Additionally, here I am also adding some reference links for your reference:

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